Language Merge

ABSTRACT

Systems and methods are described for processing and interpreting audible commands spoken in one or more languages. Speech recognition systems disclosed herein may be used as a stand-alone speech recognition system or comprise a portion of another content consumption system. A requesting user may provide audio input (e.g., command data) to the speech recognition system via a computing device to request an entertainment system to perform one or more operational commands. The speech recognition system may analyze the audio input across a variety of linguistic models, and may parse the audio input to identify a plurality of phrases and corresponding action classifiers. In some embodiments, the speech recognition system may utilize the action classifiers and other information to determine the one or more identified phrases that appropriately match the desired intent and operational command associated with the user&#39;s spoken command.

BACKGROUND

Typically, speech recognition systems are configured to process andinterpret audio input in view of the language of the consumerpopulation. Notably, many speech recognition systems are configured toprocess and interpret a single language, such as English. However, as aconsumer population becomes more diverse, it may become more of achallenge for a single language based recognition system (e.g.,English-based recognition system) to recognize and interpret audiocommands from audiences that may speak multiple languages in the samecommand/request. Furthermore, attempts to use a speech recognitionsystem trained for different languages (e.g., Spanish) may prove lesssuccessful and ineffective in a linguistic environment where multiplelanguages are comingled when spoken, such as a when a native Spanishspeaker randomly interjects English words and phrases into their speech(i.e., Spanglish). Accordingly, there remains a need to improve speechrecognition systems.

SUMMARY

The following summary is for illustrative purposes only, and is notintended to limit or constrain the detailed description. The followingsummary merely presents various described aspects in a simplified formas a prelude to the more detailed description provided below.

Features herein relate to a speech recognition system and method thatmay be used as a stand-alone speech recognition system or comprise aportion of another content consumption system. The speech recognitionsystem may facilitate requesting users to navigate through and selectcontent items available for consumption utilizing an input device (e.g.,a remote control device, smart phone device, etc.) configured toprocess, interpret and/or execute spoken commands. The speechrecognition system may interpret a spoken user command using a varietyof linguistic models (e.g., an English-based model, a Spanish-basedmodel), and may generate a transcript of the spoken command asinterpreted by each linguistic model. The speech recognition system mayextract and process a plurality of phrases from each transcript of thespoken command to determine known content entities in the base languageof the corresponding linguistic model (e.g., identifying phrasescorresponding to English content titles in the English-based acoustictranscript).

The speech recognition system may further process the plurality ofphrases extracted from each transcript to determine known action/commandentities in the base language of the corresponding linguistic model(e.g., identifying phrases corresponding to Spanish commands in theSpanish-based acoustic transcript). The speech recognition system maycombine various words/phrases from the identified phrases to generate aplurality of match phrases representing potential operational commands.The speech recognition system may use heuristic rules, command patterns,and other information (e.g., content consumption history, currentlybroadcast programming content) to filter the plurality of match phrases.After filtering the match phrases, the speech recognition system mayselect an appropriate match phrase and transmit, to a computing device,an operational command corresponding to the selected match phrase.

The summary here is not an exhaustive listing of the novel featuresdescribed herein, and are not limiting of the claims. These and otherfeatures are described in greater detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood with regard to the followingdescription, claims, and drawings. The present disclosure is illustratedby way of example, and not limited by, the accompanying figures in whichlike numerals indicate similar elements.

FIG. 1 shows an example communication network on which various featuresdescribed herein may be used.

FIG. 2 shows an example computing device that can be used to implementany of the methods, servers, entities, and computing devices describedherein.

FIGS. 3A and 3B are exemplary flow diagrams of a method in accordancewith one or more embodiments of the disclosure.

FIG. 4 shows an exemplary flow diagram and system architecture forprocessing multi-linguistic audio input according to one or moreembodiments of the disclosure.

FIG. 5 shows an exemplary flow diagram and system architecture forprocessing multi-linguistic audio input according to one or moreembodiments of the disclosure.

FIG. 6 shows an exemplary flow diagram and system architecture forprocessing multi-linguistic audio input according to one or moreembodiments of the disclosure.

FIG. 7 shows an exemplary flow diagram and system architecture for anatural language processing system according to one or more embodimentsof the disclosure.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

FIG. 1 shows an example communication network 100 on which many of thevarious features described herein may be implemented. The communicationnetwork 100 may be any type of information distribution network, such assatellite, telephone, cellular, wireless, etc. One example may be anoptical fiber network, a coaxial cable network, or a hybrid fiber/coaxdistribution network. Such communication networks 100 use a series ofinterconnected communication links 101 (e.g., coaxial cables, opticalfibers, wireless, etc.) to connect the various premises 102 (e.g.,businesses, homes, consumer dwellings, etc.) to a local office orheadend 103. The local office 103 may transmit downstream informationsignals onto the communication links 101, and each of the variouspremises 102 may have a receiver used to receive and process thosesignals.

There may be one communication link originating from the local office103, and it may be split a number of times to distribute the signal tothe various premises 102 in the vicinity (which may be many miles) ofthe local office 103. The communication links 101 may include componentsnot illustrated, such as splitters, filters, amplifiers, etc. to helpconvey the signal clearly, but in general each split introduces a bit ofsignal degradation. Portions of the communication links 101 may also beimplemented with fiber-optic cable, while other portions may beimplemented with coaxial cable, other lines, or wireless communicationpaths.

The local office 103 may include an interface 104, such as a terminationsystem (TS) interface 104. More specifically, the interface 104 may be acable modem termination system (CMTS), which may be a computing deviceconfigured to manage communications between devices on the network oflinks 101 and backend devices such as servers 105-107 (to be discussedfurther below). The interface 104 may be as specified in a standard,such as the Data Over Cable Service Interface Specification (DOCSIS)standard, published by Cable Television Laboratories, Inc. (a.k.a.CableLabs), or it may be a similar or modified device instead. Theinterface 104 may be configured to place data on one or more downstreamfrequencies to be received by modems at the various premises 102, and toreceive upstream communications from those modems on one or moreupstream frequencies.

The local office 103 may also include one or more network interfaces108, which can permit the local office 103 to communicate with variousother external networks 109. These external networks 109 may include,for example, networks of Internet devices, telephone networks, cellulartelephone networks, fiber optic networks, local wireless networks (e.g.,WiMAX), satellite networks, and any other desired network, and thenetwork interface 108 may include the corresponding circuitry needed tocommunicate on the external networks 109, and to other devices on thenetwork such as a cellular telephone network and its corresponding cellphones.

As noted above, the local office 103 may include a variety of computingdevices 105-107, such as servers, that may be configured to performvarious functions. For example, the local office 103 may include a pushnotification computing device 105. The push notification device 105 maygenerate push notifications to deliver data and/or commands to thevarious premises 102 in the network (or more specifically, to thedevices in the various premises 102 that are configured to detect suchnotifications). The local office 103 may also include a content servercomputing device 106. The content device 106 may be one or morecomputing devices that are configured to provide content to users attheir premises. This content may be, for example, video on demandmovies, television programs, songs, text listings, etc. The contentserver computing device 106 may include software to validate useridentities and entitlements, to locate and retrieve requested content,to encrypt the content, and to initiate delivery (e.g., streaming) ofthe content to the requesting user(s) and/or device(s). Indeed, any ofthe hardware elements described herein may be implemented as softwarerunning on a computing device.

The local office 103 may also include one or more application servercomputing devices 107. The application server 107 may be a computingdevice configured to offer any desired service, and may run variouslanguages and operating systems (e.g., servlets and JSP pages running onTomcat/MySQL, OSX, BSD, Ubuntu, Red Hat, HTML5, JavaScript, AJAX andCOMET). For example, an application server may be responsible forcollecting television program listings information and generating a datadownload for electronic program guide listings. The application servermay be responsible for monitoring user viewing habits and collectingthat information for use in selecting advertisements. The applicationserver may also be responsible for formatting and insertingadvertisements in a video stream being transmitted to the variouspremises 102. Although shown separately, one of ordinary skill in theart will appreciate that the push notification device 105, contentserver computing device 106, and the application server 107 may becombined. Further, here the push notification device 105, the contentserver computing device 106, and the application server 107 are showngenerally, and it will be understood that they may each contain memorystoring computer executable instructions to cause a processor to performsteps described herein and/or memory for storing data.

The example premise 102 a, such as a home, may include an interface 120.The interface 120 may include any communication circuitry needed toallow a device to communicate on one or more communication links 101with other devices in the network. For example, the interface 120 mayinclude the modem 110, which may include transmitters and receivers usedto communicate on the communication links 101 and with the local office103. The modem 110 may be, for example, a coaxial cable modem (forcoaxial cable lines 101), a fiber interface node (for fiber optic lines101), twisted-pair telephone modem, cellular telephone transceiver,satellite transceiver, local Wi-Fi router or access point, or any otherdesired modem device. Also, although only one modem is shown in FIG. 1,a plurality of modems operating in parallel may be implemented withinthe interface 120. Further, the interface 120 may include a gatewayinterface device 111. The modem 110 may be connected to, or be a partof, the gateway interface device 111. The gateway interface device 111may be a computing device that communicates with the modem(s) 110 toallow one or more other devices in the premises 102 a, to communicatewith the local office 103 and other devices beyond the local office 103.The gateway interface device 111 may be a set-top box (STB), digitalvideo recorder (DVR), computer server, or any other desired computingdevice. The gateway interface device 111 may also include (not shown)local network interfaces to provide communication signals to requestingentities/devices in the premises 102 a, such as the display devices 112(e.g., televisions), STB and/or DVR 113, the personal computers 114, thelaptop computers 115, the wireless devices 116 (e.g., wireless routers,wireless laptops, notebooks, tablets and netbooks, cordless phones(e.g., Digital Enhanced Cordless Telephone—DECT phones), mobile phones,mobile televisions, personal digital assistants (PDA), etc.), thelandline phones 117 (e.g. Voice over Internet Protocol—VoIP phones), thetablet computing devices 118, the mobile phones 119, and any otherdesired devices. Examples of the local network interfaces includeMultimedia over Coax Alliance (MoCA) interfaces, Ethernet interfaces,universal serial bus (USB) interfaces, wireless interfaces (e.g., IEEE802.11, IEEE 802.15), analog twisted pair interfaces, Bluetoothinterfaces, and others.

FIG. 2 shows general hardware elements that may be used to implement anyof the various computing devices discussed herein. The computing device200 may include one or more processors 201, which may executeinstructions to perform any of the features described herein. Theinstructions may be stored in any type of computer-readable medium ormemory, to configure the operation of the processor 201. For example,instructions may be stored in a read-only memory (ROM) 202, the randomaccess memory (RAM) 203, the removable media 204, such as a UniversalSerial Bus (USB) drive, compact disk (CD) or digital versatile disk(DVD), floppy disk drive, or any other desired storage medium.Instructions may also be stored in an attached (or internal) hard drive205. The computing device 200 may also include a security processor (notshown), which may execute instructions of a one or more computerprograms to monitor the processes executing on the processor 201 and anyprocess that requests access to any hardware and/or software componentsof the computing device 200 (e.g., ROM 202, RAM 203, the removable media204, the hard drive 205, the device controller 207, a network circuit209, the GPS 211, etc.). The computing device 200 may include one ormore output devices, such as the display 206 (e.g., an externaltelevision), and may include one or more output device controllers 207,such as a video processor. There may also be one or more user inputdevices 208, such as a remote control, keyboard, mouse, touch screen,microphone, etc. The computing device 200 may also include one or morenetwork interfaces, such as the network circuit 209 (e.g., a networkcard) to communicate with an external network 210. The network circuit209 may be a wired interface, wireless interface, or a combination ofthe two. In some embodiments, the network circuit 209 may include amodem (e.g., a cable modem), and the external network 210 may includethe communication links 101 discussed above, the external network 109,an in-home network, a provider's wireless, coaxial, fiber, or hybridfiber/coaxial distribution system (e.g., a DOC SIS network), or anyother desired network. Additionally, the device may include alocation-detecting device, such as a global positioning system (GPS)microprocessor 211, which may be configured to receive and processglobal positioning signals and determine, with possible assistance froman external server and antenna, a geographic position of the device.

Further, the computing device 200 may include an audio or speechrecognition system, such as the speech recognition engine 212, which canbe configured to receive and process audio data captured by an audiocapturing device. In some embodiments, the audio capturing device may behoused within and/or operatively connected to a user input device (e.g.,the user input device 208) or other computing device capable ofreceiving and processing command data. The speech recognition engine 212may be operatively connected to and/or in communication with a remoteserver or computer device. The remote server or computing device may beutilized to process the entire or some portion of the command dataprovided by the user.

The example in FIG. 2 is a hardware configuration, although theillustrated components may be implemented as software as well.Modifications may be made to add, remove, combine, divide, etc.components of the computing device 200 as desired. Additionally, thecomponents illustrated may be implemented using basic computing devicesand components, and the same components (e.g., processor 201, ROMstorage 202, display 206, speech recognition engine 212, etc.) may beused to implement any of the other computing devices and componentsdescribed herein. For example, the various components herein may beimplemented using computing devices having components such as aprocessor executing computer-executable instructions stored on acomputer-readable medium, as illustrated in FIG. 2. Some or all of theentities described herein may be software based, and may co-exist in acommon physical platform (e.g., a requesting entity may be a separatesoftware process and program from a dependent entity, both of which maybe executed as software on a common computing device).

One or more aspects of the disclosure may be embodied in acomputer-usable data and/or computer-executable instructions, such as inone or more program modules, executed by one or more computers or otherdevices. Generally, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types when executed by a processor ina computer or other data processing device. The computer executableinstructions may be stored on one or more computer readable media suchas a hard disk, optical disk, removable storage media, solid statememory, RAM, etc. As will be appreciated by one of skill in the art, thefunctionality of the program modules may be combined or distributed asdesired in various embodiments. In addition, the functionality may beembodied in whole or in part in firmware or hardware equivalents such asintegrated circuits, field programmable gate arrays (FPGA), and thelike. Particular data structures may be used to more effectivelyimplement one or more aspects of the disclosure, and such datastructures are contemplated within the scope of computer executableinstructions and computer-usable data described herein. The variouscomputing devices, servers and hardware described herein may beimplemented using software running on another computing device.

As noted above, features herein relate generally to a speech recognitionsystem for interpreting multiple languages comingled within a spokencommand. FIGS. 3A and 3B show an example method of processing commanddata. The example method may be performed by any computing device, forexample the computing device 200, the application server 107, etc. Inthe example depicted in FIGS. 3A and 3B, a user may be consuming contenton a television (e.g., the display device 112 via the gateway interfacedevice 111) or via the STB/DVR 113, and may receive content and/or otherinformation from servers 105-107, or another suitable computing device.Additionally, a user may utilize an input device (e.g., the input device208) to provide control input, audio input (e.g., audible commands)and/or other commands to the computing device 200. In some embodiments,input device 208 may be equipped with a microphone (or any othersuitable audio capturing device) to detect and capture spoke or commanddata. In some embodiments, the microphone or other suitable audiocapturing device may be mounted on and/or operatively connected to atransmitter for converting a user's audible or voice command into anelectronic voice signal (e.g., command data). Additionally oralternatively, the system may further comprise a recognition circuit forgenerating a control signal corresponding to a voice pattern representedby the electronic voice signal generated by the transmitter.Accordingly, the generated control signal may be transmitted to areceiver and/or any other suitable computing device (e.g., the computingdevice 200).

When describing the steps of the method below, the term “system” may beused when referring to each component, either singularly orcollectively, employed at each step of the method. Such usage is merelyfor ease in discussing the method, and should not be interpreted aslimiting the exact embodiment in which the following method may beperformed.

The speech recognition system described herein may be configured toprocess spoken commands using a variety of linguistic models inparallel. For each linguistic model (e.g., acoustic model) used tointerpret the command data, the system may generate a resulting acoustictranscript, and may parse the transcript to identify a plurality ofphrases comprising the user's spoken command. The system may dynamicallymodify/generate acoustic models and/or determiner other speechrecognition enhancements based on user preferences, viewing habits,and/or other user feedback. As will be explained in more detail below,the system may generate and/or modify acoustic models for particularsub-populations of users based on user demographic information and/orlocation. Different acoustic models for particular sub-populations ofspeakers (and/or other languages) can be created to further enhance andincrease the accuracy of the speech recognition system. For example, thesystem may generate and/or modify an acoustic model based on data andother feedback relating to a sub-population of users residing in aparticular region. As another example, the system may generate and/ormodify an acoustic model based on data and/or other feedback relating toa sub-population of users within an age group (e.g., users between theages of 8-12). The system may dynamically adjust and/or select anacoustic model based on previous audible commands for a user. The systemmay be configured to select a first acoustic model for a user based on anumber of previous queries (e.g., audible commands) provided by theuser. The system may process the previous audible commands as well asany additional audible commands provided by the user using a pluralityof other acoustic models to identify the acoustic model, and in someinstances the acoustic transcription, which best corresponds to (e.g.,matches) the user's spoken command.

The system may process the plurality of identified phrases for eachacoustic transcript to identify action entities, content entities,and/or other types of phrase classifications (e.g., action classifiers).Furthermore, utilizing a variety of heuristic cues and rule databases,the system may identify and/or rank the phrases that match the intendedcommands and/or content items desired by the user. By processing commanddata across various acoustic models, the system may detect words/phraseswithin an audible command that are spoken in different languages by theuser. As will be explained in more detail below, the system may access adictionary (or database) of words corresponding to an acoustic modelwhen processing the command data to generate an acoustic transcript. Insome instances, the system may detect a word or phrase that is not foundwithin an acoustic model recognized by the system. In such instances,the system may attempt to identify one or more phrases that bestcorresponds to the command spoken by the user.

Referring to FIG. 3A, at step 301, an initial configuration of thesystem may be performed. This initial configuration may include avariety of actions. For example, a speech recognition application may beinstalled onto the user's computing device, such as the user's STB/DVR,or may be installed onto a suitable computing device operativelyconnected to a display device (e.g., the display device 112). In someembodiments, the speech recognition application may be a component of anelectronic program guide executed on the user's computing device and/orprovided by content service. The speech recognition application mayprovide an application interface and/or a series of program menus thatpermit the user to establish audio command guidelines and/or preferencesfor controlling the STB/DVR 113, the display device 112, or othercontrollable devices in the user's premises (e.g., the premises 102 a).

In some embodiments, the user may be prompted to calibrate an inputdevice (e.g., input device 208) or any other device configured toprovide control input to a controllable device. For example, in someembodiments, the wireless device/smartphones 116 may be programmedand/or adapted to provide control input to the STB/DVR 113, the displaydevice 112, and/or any other controllable devices in the user'spremises. In these embodiments, the user may be prompted to pair theinput device and/or wireless device with a controllable device residingin the user's premises (e.g., the STB/DVR 113, the display device 112,etc.). In some embodiments, during step 301, the speech recognitionsystem may initiate a training sequence to calibrate an audio capturingcomponent of the input device and/or wireless device. In suchembodiments, the system may provide the user with a transcript of words,commands, phrases, etc., and may prompt the user to speak one or more ofthe phrases/words into the input device in furtherance of calibratingthe speech recognition application. In some embodiments, the system mayutilize command data obtained during a training sequence to generateand/or modify acoustic models utilized by the system. Additionally, oralternatively, the user may be prompted to say a plurality of audiocommands such that the speech recognition application can process andanalyze the user's speech pattern and other audio characteristics tomore accurately interpret the user's voice and identify appropriateacoustic models for the user. The system may utilize various acoustic(or linguistic) models to interpret spoken user commands. For example,as will be explained in more detail below, the system may utilizedictionary-like databases for one or more languages (e.g., English,Spanish, etc.), when attempting to recognize and interpret audiblecommands that are spoken partly in a first language and partly in asecond language (e.g., “Spanglish”).

The remote control device may have one or more sets of audible commandand/or voice templates stored in memory. For example, during or afterthe manufacture of the remote control device, an entity may download aplurality of commands and/or voice templates to the remote controldevice. In this example, the remote control device may be configured togenerate control signals representing spoken user commands, and comparethe control signals to one or more voice templates to determine theparticular words/phrases spoken by the user. Additionally oralternatively, the remote control device may communicate with one ormore remote computing devices to request and/or obtain audible commandsthat may be recognized by the remote control device. Alternatively oradditionally, the stored audible commands for the remote control devicemay be displayed by a display of the remote control device. In someembodiments, the remote control device may communicate with anapplication interface displayed on a display device (e.g., display ofthe remote control, PC, the display device 112, etc.) to permit a userto further configure and/or calibrate the remote control device. In suchembodiments, stored audible commands of the remote control device may bedisplayed on the application interface via a suitable display device,and may further provide the user with one or more menu options forerasing, adding and/or modifying the commands recognized by the remotecontrol device. In other such embodiments, it may also possible that theaudible commands for the remote control device can be displayed ortransferred to the display device.

The initial system configuration may also include establishing therequesting user's account with a content service (e.g., creating anaccount, with username and password or other security, to allow the userto consume content on devices such as the STB/DVR 113) which may be, forexample, a service operated by the content server 106 that providesstreaming video content to the requesting user's authorized devices. Onthe topic of authorized devices, the initial configuration may alsoentail registering the STB/DVR 113, the computers 114/115, the wirelessdevices/smartphones 116, the input device 208, and any other device atthe user's premises. This device registration may include providing thecontent server 106 with address information for the devices (e.g., aphysical address, media access control (MAC) address, etc.), informationidentifying the device's capabilities (e.g., types of user interactioninputs/outputs available, memory size, processor type, form factor,display size and resolution, number of speakers, etc.).

During step 301, the system may populate a database (e.g., languagedatabase) of words/phrases for the various languages recognized by thesystem. The database may serve as a multi-lingual dictionary storingvarious phrases and/or commands that may be spoken by a user andrecognized by the system. For example, as will be explained in moredetail below, the system may include a processor for executing a speechrecognition and/or natural processing algorithm to recognize audiblecommands detected by the remote control device (or other suitable audiocapturing device). As noted above, the language database may include adatabase of voice templates corresponding to voice signatures for one ormore words/phrases in the database. The system may operativelycommunicate with a network or Internet-based database to retrieve voicetemplates and/or audible commands. Additionally or alternatively, insome embodiments, the database may also store a plurality of acoustic(or linguistic) models associated with a language (e.g., English,Spanish, etc.) corresponding to the database. The voice templatescorresponding to voice signatures for various phrases may be stored in aseparate database from the plurality of acoustic models utilized tointerpret command data. The system may utilize acoustic models stored ina first database to search and/or retrieve particular phrases and/orvoice templates stored in a second database when analyzing andinterpreting spoken user commands.

In step 303, the system may receive command data (e.g., audio input)from a first input device. During step 303, the user may provide anaudible command to an input device, such as the input device 208, thewireless device 116, and/or any other computing device configured toreceive and process audible commands from a user. In some embodiments,to initiate a recording of a spoken command, the user may first actuatea button on the input device. In other examples, the user may actuate afirst designated area of an on-screen application interface (e.g., arecord icon) to initiate recording of the audible command. The speechrecognition module may reside within the input device. In suchembodiments, the command data may be processed and analyzed at the inputdevice to determine the appropriate command and/or control informationto transmit to a computing device. In other embodiments, the inputdevice may process and transmit the received command data to anothercomputing device, such as the device 200, for further processing by aspeech recognition engine.

FIG. 4 shows an exemplary flow diagram and system architecture forprocessing multi-linguistic audio input. In the example embodimentdepicted in FIG. 4, at least two language databases and correspondingacoustic models (e.g., English-based acoustic models and Spanish-basedacoustic models) may be used to process a command spoken in English(i.e., a user saying “Watch Nickelodeon”) captured at an input device.For example, referring to FIG. 4, the input device 410 is equipped witha microphone (or other audio capturing device) configured to receive andprocess audible command made by a user. At element 401, the input devicemay transmit command data corresponding to a spoken command (e.g.,“Watch Nickelodeon”) to the STB/DVR 411. The input device may beconfigured to transmit command data to a remote server for processing.The STB/DVR may access and/or request the command data captured by theinput device 410 from the remote server. After receiving the commanddata, as shown by element 402, the STB/DVR 411 may transmit the commanddata to speech recognition module 412 for further processing. As notedabove, a speech recognition module (e.g., the speech recognition module412, the speech recognition engine 212, etc.) may be executed at acomputing device, such as the computing device 200 to process and/orinterpret command data received from the ST STB/DVR B 411.

The speech recognition module may be configured to process command dataand generate some form of output data. The speech recognition module mayprocess a plurality of voice templates to determine the particular wordsbeing spoken by the user. In some instances, the speech recognitionmodule may be unable to ascertain voice templates that best correspondsto the spoken user commands, and may attempt to find the words/phrasesthat appropriately match the command data representing the user's spokencommand. In some embodiments, the speech recognition module mayconfigured to identify and output the words/phrases that appropriatelymatch the command data notwithstanding how poorly an identifiedword/phrase actually matches the analyzed command data.

FIG. 5 shows an exemplary flow diagram and system architecture forprocessing multi-linguistic audio input. In the example embodimentdepicted in FIG. 5, at least two language databases and correspondingacoustic models (e.g., English-based acoustic models and Spanish-basedacoustic models) may be used to process a command spoken in Spanish(i.e., a user saying “Ver Univision”) captured at an input device. Atelement 501, the input device may transmit to the STB/DVR 511 commanddata corresponding to a spoken command (e.g., “Ver Univision”). Afterreceiving the command data, as shown by element 502, the STB/DVR 511 maytransmit the command data to the speech recognition module 512 forfurther processing.

Similarly, FIG. 6 shows an exemplary flow diagram and systemarchitecture for processing multi-linguistic audio input. In the exampleembodiment depicted in FIG. 6, at least two language databases andcorresponding acoustic models (e.g., English-based acoustic models andSpanish-based acoustic models) may be used to process a command spokenpartly in English (i.e., a user saying “Watch”) and partly in Spanish(i.e., the user pronouncing the acronym HBO in Spanish—“Ach Bay Oh”)captured at an input device. At element 601, the input device maytransmit command data corresponding to a spoken command (e.g.,“Watch+Ach Bay Oh”) to the STB/DVR 611. After receiving the commanddata, as shown by element 602, the STB/DVR 611 may transmit the commanddata to the speech recognition module 612 for further processing.

Referring back to FIG. 3A, at step 305, the system may determine one ormore acoustic models to be utilized by a speech recognition engine. Thesystem may prompt the user to identify one or more acoustic models thatmay be utilized to interpret audible commands provided to the inputdevice. The system may instruct a display device to display to the usera listing of different acoustic models, and may prompt the user toselect one or more models from the list. For example, a speechrecognition application may instruct a display device to display aplurality of different languages for a user to select from. As will beexplained in more detail below with reference to FIGS. 4 and 7, in someembodiments, the system may utilize a natural language processor, eitheralone or in combination with a speech recognition engine module, toprocess and interpret command data. In some of these embodiments, anatural language processor may be configured to identify the one or moreacoustic models utilized by the system to interpret command data.Additionally or alternatively, the system may identify one or morelanguage databases that may be utilized to interpret audible commandsprovided to an input device. In such embodiments, the system mayretrieve one or more acoustic models associated with (and/or stored at)a particular language database. For example, a speech recognition modulemay retrieve (or request) English-based acoustic models from anEnglish-based language database.

In some instances, the system may automatically identify an acousticmodel to be utilized by the speech recognition system based on a varioustypes of information without departing from the scope of the presentdisclosure, such as user profile, the user's content preferences, theuser's viewing habits, and other suitable user information. The systemmay identify an acoustic model based on the user's account profileand/or content preferences. The system may also identify a languagedatabase from which to retrieve an acoustic model based on the user'saccount profile and/or preferences For example, the system mayautomatically identify a Spanish-based language database and retrievecorresponding Spanish-based acoustic models when the system determinesthat the user has activated a Spanish secondary audio stream (“SAP”)feature on the STB/DVR 411 or an operatively connected display device(e.g., the display device 112). As another example, the system mayautomatically identify an acoustic model to be utilized by the speechrecognition system based on a language selected to be displayed to theuser via a programming content interface (e.g., electronic programguide). For example, the system may select an acoustic (or linguistic)model to interpret command data based on the gender of the user. In thisexample, the system may select from a first set of linguistic modelsassociated with the gender of the user.

In other embodiments, the system may be configured to automaticallyselect an acoustic model for a user based on the user's audiofingerprint. Over time, the system may receive, process, and storeprevious audible commands of the user. The system may be configured toutilize the previous audible commands of the user to identify certainaudible characteristics and/or parameters unique to the user. In some ofthese embodiments, the system may analyze the user's audible command,and may extract a portion or segment of the audible command (e.g., audiofingerprint) for further processing. In some instances, the system maycompare characteristics of an extracted portion of the audible commandwith audio characteristics of a stored audio sample to confirm theidentity of the user. In other instances, the system may select anacoustic model based on a comparison of audio characteristics in anextracted portion of the audible command and audio characteristics ofstored audio samples and/or acoustic models.

The system may identify an acoustic model for a user based on a time ofday and/or the particular day that the audible command is provided bythe user. The system may analyze a stored content consumption historyfor a plurality of viewers to anticipate which viewer is providing theaudible command. For example, if the system receives an audible commandat 11:00 pm, the system may analyze a content consumption history forthe one or more viewers associated with the account to determine whichusers (if any) have previously consumed content and/or provided anaudible command during that time period. In this example, younger users(e.g., children) may not typically be awake during this time period, asevidenced by the stored content consumption history indicating thatprogramming content for children has not been consumed after a certaintime period, or that a younger user on the account has not provided anaudible command requesting content after a certain time period. As such,the system may be configured to exclude certain users (e.g., youngerusers) when attempting to identify a user that provided the audiblecommand. According, the system may utilize the stored contentconsumption history to identify which user provided the audible command,and further, to select the appropriate acoustic model for that user.

In another embodiment, the system may select a linguistic model based ona geographic location of the user. The system may select from a firstset of linguistic models associated with the zip code, city, stateand/or geographic region of the user providing the command data. Forexample, when selecting linguistic models to interpret command dataprovided by a user living Boston, the system may emphasize (and/orprioritize) linguistic models associated with users having a Boston orNew England regional accent. As another example, when selectinglinguistic models to interpret command data provided by a user livingSouth Carolina, the system may emphasize (and/or prioritize) linguisticmodels associated with users having a Southern accent. Additionally oralternatively, the system may select a linguistic model based on alanguage preference and/or other characteristics of the user. Forexample, if the user is a native-English speaker, when selectinglinguistic models to interpret command data provided by the user, thesystem may select from a first language database and/or set oflinguistic models associated with native-English speaking users.

As another example, if the user is a native-Spanish speaker, whenselecting linguistic models to interpret command data provided by theuser, the system may select from a first language database and/or set oflinguistic models associated with native-Spanish speaking users. Theproper acoustic model(s) to be utilized by the system may be identifiedin a variety of manners based on the user characteristics, demographics,and profile/account information as discussed above. For example, whendetermining that a user utilizes both Spanish and English words/phraseswithin their spoken commands and/or operation of the STB/DVR, the systemmay utilize acoustic models associated with both a native-Spanishspeaker and a native-English speaker to interpret the user's spokencommands. Accordingly, such acoustic models may be utilized by thesystem if the user was accustomed to using Spanglish when providingspoken commands.

In other embodiments, the speech recognition system may use anative-Spanish acoustic model and a second acoustic model correspondingto a native-Spanish speaker speaking English to interpret the user'scommands. Various types and/or combinations of acoustic models may beused to best analyze and interpret spoken user commands withoutdeparting from the scope of the present disclosure. Referring to theexample above, the acoustic model corresponding to a native-Spanishspeaker speaking English may be utilized to supplement and/or replacenative-English acoustic models to more accurately recognize andinterpret user commands that are spoken in Spanglish. As anotherexample, for users that speak Spanglish and live in Texas, the systemmay utilize acoustic models associated with native-Spanish speakers,native-English speakers and/or native-English speakers with Southern orTexan accents to interpret the user's spoken commands. As discussedabove, over time, the system may calibrate which acoustic models areutilized to interpret a user's spoken commands. Additionally oralternatively, as part of the calibration process, the user may beprovided with various menus and/or options (via an applicationinterface) to facilitate the calibration process, and further to provideadditional information such that the system may select the mostappropriate acoustic models.

Referring back to FIG. 4, the speech recognition module 412 may identifyone or more acoustic models to be utilized for processing and/orinterpreting command data. In the example embodiment depicted in FIG. 4,the system may utilize at least a first linguistic (or acoustic) modelassociated with native-English speaking users (e.g., the acoustic model415) to interpret the audible command received at the STB/DVR 411 (seeelement 401), and may further utilize a second linguistic modelassociated with native-Spanish speaking users (e.g., the acoustic model418) to interpret the audible command. The speech recognitionapplication may prompt the user to confirm an automatically identifiedlinguistic model for processing the command data.

A content provider may utilize third-party vendors or services tocreate/implement the speech recognition technology and/or software usedin conjunction with the user's entertainment system. In such instances,the underlying content provider may not have authorization to adjust ormodify the initial calibration system or other aspects of the speechrecognition system. Accordingly, the content provider may utilize anatural language processor and/or other suitable computing device toprocess output provided from a third-party speech recognition system tomodify or calibrate certain aspects of the speech recognition system.

As noted above, in some embodiments, the system may utilize a naturallanguage processor (e.g., the NLP 420) to analyze the command data. Thenatural language processor may utilize user profile/account information,viewing habits, content consumption history, and other information toidentify the appropriate acoustic models for processing command data.The natural language processor may utilize command data provided asoutput from a speech recognition module to identify the appropriateacoustic models. As shown in FIG. 4, the NLP 420 may receive and processoutput (e.g., command data) from the speech recognition module 412.Additionally or alternatively, the NLP 420 may process output from thespeech recognition module 412 to determine one or more acoustic modelsto be utilized for interpreting the command data. In still otherexamples, the natural language processor may utilize command datareceived from an external computing device to identify the appropriateacoustic models for interpreting spoken user commands.

The NLP 420 may be programmed and adapted to execute a speechrecognition and/or natural language processing algorithm to analyzecommand data. The natural language processing algorithm may be utilizedby the NLP 420 to generate valid hypotheses as to what actions a userdesires the entertainment system to implement based on the user's spokencommands. The natural language processing algorithm may be configured toidentify, sort, and/or rank the various possible commands and actionsthat may be implemented by the entertainment system (and/or otheroperatively connected computing devices) based on the user's spokencommand. As will be explained in more detail below with respect to FIG.7, when executing the natural language processing algorithm, the NLP 420may receive (or request) one or more linguistic transcriptions of theuser's spoken commands in a variety of languages, and may furtherattempt to recognize and parse keywords and phrases from each receivedtranscription. The detected keywords may comprise action entities,content entities, or other types of phrase classification or actionclassifiers.

When recognizing keywords for multiple languages, the natural languageprocessing algorithm may use a variety of heuristic rules and/or otherinformation to rank the most appropriate phrases (and/or correspondinglanguage). The natural language processing algorithm may use these rulesto select the top entities and/or phrases that match a user's intentbased on context. For example, if the natural language processingalgorithm detects the word “Watch” in English (or similarly the word“Ver” in Spanish) the system may categorize this term as a first type ofaction classifier (e.g., an action entity), and may then search for asecond type of action classifier (e.g., content entities) in one or moreremaining portions of the acoustic transcript corresponding to theuser's spoken command. As another example, if the natural languageprocessing algorithm detects the word “Watch” in English (or similarlythe word “Ver” in Spanish) the system may categorize this term as afirst type of action classifier (e.g., an action entity), and may thenassign a second type of action classifier (e.g., content entities) tothe one or more remaining portions of the acoustic transcriptcorresponding to the user's spoken command. In the examples above, ifthe natural language processing algorithm determines that the name of asports team is also included in the transcript, the natural languageprocessing algorithm may infer that the user intends to watch a sportingmatch that features the identified sports team. As another example, ifthe natural language processing algorithm detects the word “Watch” andthe name of a content title (e.g., a TV program, movie, etc.) areincluded in the transcript, the natural language processing algorithmmay infer that the user intends to watch the content title and/or forthe entertainment system to begin playing the identified content title.

As noted above, the natural language processing algorithm may generatemultiple hypotheses based on the detected keywords, heuristic rules,and/or the inferred intent of the user. For example, as will beexplained in more detail below, in view of certain heuristic rulesutilized by the system, such as the availability of programming content(e.g., content items) and/or the type of acoustic model used tointerpret the command data, the natural language processing algorithmmay discard, promote, and/or deemphasize each possible hypothesesgenerated by the natural language processing algorithm. As will beexplained din more detail below, one or more of the hypotheses generatedby the natural language processing algorithm may be transmitted to adisplay device to be viewed by the user. The system may receive userinput selections indicating a most appropriate hypothesis in view of theuser's actual intent. The system may be configured to store the user'sselections, and over time, the system may utilize user feedback tocalibrate the natural language processing algorithm and/or acousticmodels used to interpret command data.

Referring back to FIG. 3A, at step 307, the system may begin a loop thatis performed for one or more of the acoustic models identified in step305. In one embodiment, a speech recognition module (such as the speechrecognition engine 212), may be configured to begin a loop that isperformed for one or more of the acoustic models identified in step 305.Additionally or alternatively, a computing device executing the speechrecognition application may be configured to begin a loop that isperformed for one or more of the monitored devices identified in step305. In step 309, for each acoustic model analyzed within the loop, thesystem (and/or speech recognition engine) may process and analyze thecommand data received during step 303. For example, as shown in FIG. 4,the system may receive an audible command from the user to “WatchNickelodeon,” (e.g., element 401), and as shown by element 403, thesystem may utilize the acoustic model 415 to analyze the command data toidentify and/or extract each phrase comprising the spoken user commandin accordance with a linguistic context of the acoustic model 415 (e.g.,an acoustic model for native-English speakers).

The NLP 420 may process command data received from the speechrecognition module 412 to identify variety of linguistic characteristicsassociated with the command data. For example, the NLP 420 may processthe command data to determine whether the user providing the spokencommand comprising the command data speaks with a particular and/orregional accent. As another example, the NLP 420 may process commanddata to distinguish what language the user is speaking. Additionally, asnoted above, the NLP 420 may utilize other data, such as user accountinformation, user viewing habits, user demographic information, locationinformation, and/or user content preferences, to identify theappropriate acoustic models for interpreting the command data. In otherembodiments, the NLP 420 may utilize heuristic cues and/or other rulesto identify acoustic models for interpreting the command data.

Additionally, referring to the example in FIG. 4, the system may utilizethe acoustic models 415 and 418 to analyze and/or interpret command dataprovided by the speech recognition module in accordance with alinguistic context of the underlying acoustic model. In this example,the system may identify two acoustic models to interpret the commanddata. The first acoustic model (e.g., the acoustic model 415) maycorrespond to the English language and/or a sub-population ofnative-English speakers. The second acoustic model (e.g., the acousticmodel 418) may correspond to the Spanish language and/or asub-population of native-Spanish speakers. In this example, the systemmay receive an audible command from the user, i.e., “Watch Nickelodeon,”(e.g., element 401), and as shown by element 403 and 404, the system mayutilize the acoustic models 415 and 418 to analyze the command data.

In some embodiments, the NLP 420 may execute a natural languageprocessing algorithm to analyze command data. FIG. 7 shows an exemplaryflow diagram and system architecture for a natural language processingalgorithm executed by a natural language processing module, for examplethe NLP 420. As will be explained in more detail below with respect toFIG. 7, the natural language processor may analyze the command data inview off heuristic rules and other information databases to identifywhich language is being spoken by the user and to determine theappropriate acoustic models to utilize when interpreting the commanddata. Referring back to FIG. 4, the natural language processing modulemay utilize acoustic models (e.g., the acoustic models 415 and 418) toprocess and/or interpret command data. As noted above, in someembodiments, SR 412 may process command data received from the STB/DVR411 (or any other suitable computing device) to generate an acoustictranscript of the spoken command comprising the command data. The speechrecognition module 412 may utilize an acoustic model to process thecommand data and to generate an acoustic transcript corresponding to thecommand data. Additionally or alternatively, the NLP 420 may receivefrom the speech recognition module 412 acoustic transcriptscorresponding to each of the acoustic models utilized by the system toprocess the command data.

In still other embodiments, the NLP 420 may process command datareceived from the speech recognition module 412 (or any other suitablecomputing device) to generate an acoustic transcript of the spoken usercommand comprising the command data. The NLP 420 may utilize acousticmodels to interpret command data, and may further generate an acoustictranscript of the command data for one or more of the acoustic modelsused to analyze the command data. Referring to the examples in FIGS. 4and 7, the NLP 420 may interpret command data utilizing a first acousticmodel (e.g., acoustic model 415) to generate an acoustic transcript(e.g., the acoustic transcript 701). In this example, the NLP 420 mayinterpret command data corresponding to the spoken user command (e.g.,“Watch Nickelodeon”) utilizing an acoustic model for native-Englishspeakers (e.g., the acoustic model 415) to generate a transcript inEnglish of the user's command. Additionally or alternatively, the NLP420 may utilize an English language database and/or a correspondingdatabase of English-based voice templates to analyze the spoken commanddata. The NLP 420 may compare the command data (e.g., electronic signalsrepresenting the spoken command “Watch Nickelodeon”) to one or morevoice templates to identify the various words or phrases being spoken bythe user. Accordingly, as shown in FIG. 4, the NLP 420 may determinethat the acoustic transcript 701 comprises the phrases/words “WatchNickelodeon.”

Similarly, the NLP 420 may interpret the command data utilizing otheracoustic models (e.g., the acoustic model 418) to generate additionalacoustic transcripts (e.g., the acoustic transcript 705). In thisexample, the NLP 420 may interpret command data corresponding to thespoken user command (e.g., “Watch Nickelodeon”) utilizing an acousticmodel for native-Spanish speakers (e.g., the acoustic model 418) togenerate a transcript in Spanish of the user's spoken command. The NLP420 may utilize a Spanglish language database and/or a correspondingdatabase of voice templates to analyze the command data. The NLP 420 maycompare the command data (e.g., electronic signals representing thespoken command “Watch Nickelodeon”) to one or more voice templates toidentify the various words or phrases being spoken by the user inSpanish. In this instance, although the user's command were spoken inEnglish, the NLP 420 may attempt to find the Spanish words/phrasescorresponding to Spanish-based voice templates that appropriately matchthe analyzed command data. Accordingly, as shown in FIG. 4, the NLP 420may determine that the acoustic transcript 705 comprises thephrases/words “<unknown>+Meneca Lorient.”

Referring to the example embodiment in FIG. 5, the NLP 520 may interpretcommand data utilizing a first acoustic model (e.g., the acoustic model515) to generate an acoustic transcript. In this example, the NLP 520may interpret command data corresponding to the spoken user command(e.g., “Ver Univision”) utilizing an acoustic model for native-Englishspeakers (e.g., the acoustic model 515) to generate a transcript inEnglish of the user's spoken command. Additionally or alternatively, theNLP 520 may utilize an English language database and/or a correspondingdatabase of English-based voice templates to analyze the spoken commanddata. The NLP 520 may compare the command data (e.g., electronic signalsrepresenting the spoken command “Ver Univision”) to one or more voicetemplates to identify the various words or phrases being spoken by theuser. Accordingly, as shown in FIG. 5, the NLP 520 may determine that aresulting acoustic transcript may comprises the phrases/words “Where”and “Unit Vision.” In this example, the system identified that a firstportion of the command data corresponding to the spoken command “Ver”appropriately matched an English-based voice template corresponding tothe English word “Where.” Similarly, the system identified that a secondportion of the command data corresponding to the spoken command“Univision” appropriately matched an English-based voice templatecorresponding to the English words “Unit Vision.”

In the example embodiment depicted in FIG. 5, the NLP 520 may interpretthe command data utilizing other acoustic models (e.g., the acousticmodel 518) to generate additional acoustic transcripts. In this example,the NLP 520 may interpret command data corresponding to the spoken usercommand (e.g., “Ver Univision”) utilizing an acoustic model fornative-Spanish speakers (e.g., the acoustic model 518) to generate atranscript in Spanish of the user's command. The NLP 520 may utilize aSpanglish language database and/or a corresponding database of voicetemplates to analyze the spoken command data. The NLP 520 may comparethe command data (e.g., electronic signals representing the spokencommand “Ver Univision”) to one or more voice templates to identify thevarious words or phrases being spoken by the user in Spanish. In thisexample, as shown in FIG. 4, the NLP 420 may determine that a resultingacoustic transcript of the spoken command may comprise the phrases/words“Ver Univision.”

Similarly, referring now to the example embodiment in FIG. 6, the NLP620 may interpret command data utilizing a first acoustic model (e.g.,the acoustic model 615) to generate an acoustic transcript. In thisexample, the NLP 620 may interpret command data corresponding to a usercommand spoken in Spanglish, where the user is attempting to watch thetelevision network HBO. The system may first process the receivedcommand data utilizing an acoustic model for native-English speakers(e.g., the acoustic model 615) to generate a transcript in English ofthe user's spoken command. Additionally or alternatively, the NLP 620may utilize an English language database and/or a corresponding databaseof English-based voice templates to analyze the command data. The NLP620 may compare the command data (e.g., electronic signals representingthe spoken command) to one or more voice templates to identify thevarious words/phrases being spoken by the user. Accordingly, as shown inFIG. 6, the NLP 620 may determine that a resulting acoustic transcriptmay comprise the phrases/words “Watch” and “Ach Bay Oh.” In thisexample, the system identified that a first portion of the command datacorresponding to the spoken command “Watch” appropriately matched anEnglish-based voice template corresponding to the English word “Watch.”Similarly, the system identified that a second portion of the audiblecommand appropriately matched an English-based voice templatecorresponding to the English phonetic pronunciation of “Ach+Bay+Oh.”

In the example embodiment depicted in FIG. 6, the NLP 520 may interpretthe command data utilizing other acoustic models (e.g., the acousticmodel 618) to generate additional acoustic transcripts. In this example,the NLP 620 may interpret command data corresponding to the spoken usercommand utilizing an acoustic model for native-Spanish speakers (e.g.,the acoustic model 618) to generate a transcript in Spanish of theuser's command. The NLP 620 may utilize a Spanglish language databaseand/or a corresponding database of voice templates to analyze the spokencommand data. The NLP 620 may compare the command data (e.g., electronicsignals representing the spoken command) to one or more voice templatesto identify the various words or phrases being spoken by the user inSpanish. In this instance, although the user's command were spoken inSpanglish, the NLP 420 may attempt to find the Spanish words or phrasescorresponding to Spanish-based voice templates that appropriately matchthe analyzed command data. Accordingly, as shown in FIG. 4, the NLP 420may determine that a resulting acoustic transcript for a first portionof the command data is unrecognizable to the system. However, for asecond portion of the command data the system may determine that aresulting acoustic transcript of the spoken command may comprises thephrase/word “HBO.”

Referring back to FIG. 3A, at step 311, the system may identify and/orextract key phrases from the command data processed during step 309. Thesystem may analyze the command data received during step 309 and mayparse the received command data into a plurality of phrases. Forexample, the system may utilize a natural language processor (or othersuitable language processing device) to analyze and/or parse each phraseof an acoustic transcript corresponding to the spoken user commands.Although the term “phrase” is used herein to describe a segment of thecommand data that may be identified/extracted from command data or anacoustic transcript by the system, a “phrase” may comprise one or morewords. Alternatively or additionally, a phrase may comprise one or morewords which can be part of an entity and/or context recognized by theentertainment system. Various known methods for analyzing command datato identify and/or parse phrases may be implemented in accordance withthe embodiments disclosed herein.

As discussed above with reference to FIGS. 4 and 7, the system mayutilize language databases and corresponding acoustic models tointerpret command data and to generate acoustic transcripts of the auser's spoken commands. The system may utilize an acoustic model (e.g.,the acoustic model 415) to analyze the command data to generate anacoustic transcript of the user's spoken command. The natural languageprocessor may analyze the acoustic transcript to identify and/or extracteach phrase comprising the spoken user command. For example, as shown inFIG. 7, the phrase extractor 710 may analyze acoustic transcript 701 toidentify and/or extract each phrase comprising the spoken user commandin accordance with a linguistic context of the acoustic model (e.g., theacoustic model 415) used to generate the transcript. Accordingly, thephrase extractor 710 may analyze the acoustic transcript 701 to identifyand/or extract one or more English words/phrases from the transcript. Inthis example, the phrase extractor 710 may analyze a first portion ofthe acoustic transcript 701 corresponding to the spoken user command toidentify an English phrase, which in this example would correspond tothe word “Watch.” Additionally, the phrase extractor 710 may analyze aremaining portion of the acoustic transcript 701 corresponding to thespoken user command to identify an appropriate English phrase, which inthis example would correspond to the word “Nickelodeon.”

Referring back to FIG. 4, the NLP 420 may utilize information fromvarious input sources (e.g., the rules database 422, the entity database424, etc.) to analyze and parse the one or more phrases comprising thecommand data. As discussed above, the NLP 420 may utilize a phraseextractor module (e.g., the phrase extractor 710) to analyze and parseeach phrase of an acoustic transcript (e.g., the acoustic transcripts701, 705) of the user's spoken commands. The phrase extractor 710 mayutilize information from various input sources (e.g., the rules database711, the entity database 712, etc.) to analyze and parse each of theacoustic transcripts. Additionally or alternatively, an entity database(e.g., the entity database 712) may store “synonyms” that may beutilized by the system to interpret acoustic transcripts and/or audiblecommands representing spoken user commands. For example, a synonym mayrefer to a word or phrase which maps to an entity and/or command phrase,but may be different or similar to the entity/phrase in a variety ofways without departing from the scope of the present disclosure. Forexample, a synonym may differ from an entity/phrase based on a spellingdifference due to the selected linguistic model for interpreting thespoken command (e.g., “spider man”=>“spiderman”). As another example, asynonym may differ from an entity/phrase based on a spelling differencein view of the acoustic transcription process (e.g., “hairypotter”=>“harry potter”). As another example, a synonym may differ froman entity/phrase based on differences in content promotion versuscontent metadata supplied to the content provider via 3^(rd)-party dataprovider (e.g., “at midnight”=>“@midnight”). Still another example, asynonym may differ from an entity/phrase based on semantic similaritiesdue to the natural language model utilized for the user and/or based oncommon sense equivalents for phrases utilized by the user (e.g.,“watch”=>“tune to”).

As discussed above, the synonyms stored in the entity database may beutilized by the system to overcome learned and/or common errors wheninterpreting command data. As discussed above, third-party speechrecognition services may be utilized to provide speech recognitionfunctionality/capabilities for a content provider. In such instances,the content provider may not have access to the underlying speechrecognition software to calibrate the speech recognition system or tocorrect common/learned errors in recognizing commands spoken by avariety of different users. For example, a user may try to access orconsume particular programming content, such as the television channel“Black Entertainment Television,” which is often referred to inshorthand as “B.E.T.” In several instances, the speech recognitionsystem may incidentally recognize command data corresponding to a userspeaking the phrase “B.E.T.” as two separate phrases corresponding to“<unknown>”+“E.T.” Although in many of these instances, the user may beattempting to watch programming content on the television channel“B.E.T.,” because the speech recognition system may be consistentlyunable to recognize the first letter in the acronym, the speechrecognition system may process a portion of the user command as “E.T.,”which may correspond to other programming content (e.g., the movie“E.T.,” the television show Entertainment Tonight, etc.). In thisexample, a synonym may be generated and/or stored in the entity databaseto overcome the recognition issues created by the speech recognitionsystem. For instance, referring to FIG. 4, the synonym stored in theentity database 424 may be utilized to associate and/or weight commanddata recognized by the speech recognition system as “<unknown>+E.T.” tocorrespond to “B.E.T,” thus correcting the recognition issue at thenatural language processing layer and not the speech recognition layer.

Synonyms may be generated and/or created in a variety of differentmanners without departing from the scope of the present disclosure.Synonyms may be created by the system utilizing supervised methods. Thecontent service may utilize editors to annotate command data from aplurality of users. The editor may flag and/or input data into thesystem indicating various synonyms for certain words and phrases. Asanother example, the system may create a synonym in response to userreports (e.g., error reports) and/or user input indicating differencesbetween an intended command and resulting command operations performedby the system. For example, if the user provides an audible commandcorresponding to “Harry Potter,” but the system interprets the commandas “Hairy Potter,” in some instances, the user may have an opportunityto correct the interpreted command if (or when) the system prompts theuser to confirm the spoken command. In this example, if the user amendsthe command to correspond to “Harry Potter,” the system may transmit acommunication (e.g., error report) indicating this amendments. In someinstances, the system may create a synonym based on the differencesand/or similarities between the two phrases.

In other embodiments, synonyms may be automatically and/or dynamicallycreated by the system. The system may utilize an auto-correction moduleto provide real-time correction for certain entity or phrase types basedon heuristic rules and other information, such as clickrank data. Forinstance, if the user provides an audible command corresponding to“Harry Potter,” but the system interprets the command as “Hairy Potter,”the user may have an opportunity to correct the interpreted command if(or when) the system prompts the user to confirm the spoken command. Inthis instance, the system may process clickrank data for a plurality ofusers to determine an excepted or intended phrase/entity provided by theuser (e.g., “Harry Potter”), and may prompt the user to confirm thechange. Additionally or alternatively, the system may utilize amachine-learning based language model to analyze a plurality of usersessions to identify rephrasing of spoken commands. The system maygenerate synonyms based on the rephrased commands. For example, ifduring a previous user session, the system rephrased the audible commandof “Watch X Tant” to “Watch Extant,” the system may create a synonymmapping the phrase “X Tant” to “Extant.” In some instances, the systemmay prompt the user to confirm the creation and/or mapping for asynonym.

In yet another of these embodiments, the system may utilize a contextsensitive machine learning models to identify and/or correct mistakes intranscriptions of command data to produce a correct or more appropriateacoustic transcription. The system may generate synonyms based on thecorrection of an acoustic transcript. For instance, when interpretingthe audible command “Watch B.E.T. channel” the speech recognition modulemay interpret the command as “Watch E.T. channel.” In this example, asynonym may be generated and/or stored in the entity database toovercome the recognition issues created by the speech recognitionsystem. For instance, referring to FIG. 4, the synonym stored in theentity database 424 may be utilized to associate and/or weight commanddata recognized by the speech recognition system as “<unknown>+E.T.” tocorrespond to “B.E.T,” thus correcting the recognition issue at thenatural language processing layer and not the speech recognition layer.Additionally or alternatively, the system may utilize a natural languagephoneme model to identify and/or correct pronunciation errors forcertain word/phrases comprising a spoken command, and may generate oneor more synonyms based any corrections made by the system. For example,the system may create a synonym mapping the term “pixie” to “pixy” (orvice-a-versa) based on variances in a user's pronunciation of certainterms. The system may utilize a hybrid (or semi-supervised) method toidentify and/or create synonyms. For example, the system may create oneor more linguistic rules based on the above-discussed methods toautomatically convert an incorrect acoustic transcription into anintended or desired transcription, and as such, the system may not storeone-to-one mappings for each synonym to phrases/entities each time auser provides an audible command. For example, the system may beconfigured to try both “&” and “and” as replacements terms for eachother when analyzing a user's audible command and/or when generating anacoustic transcription.

Referring back to FIG. 4, as depicted by element 404, the system mayalso utilize a second acoustic model (e.g., the acoustic model 418) toanalyze and/or interpret each phrase comprising the audible command inaccordance with a linguistic context of the acoustic model 418) (e.g.,and acoustic model for native-Spanish speakers). Accordingly, the systemmay receive an audible command from the user corresponding to the spokenuser command “Watch Nickelodeon,” (e.g., element 401), and the systemmay utilize the acoustic model 418 to analyze the command data, generatean acoustic transcript in Spanish corresponding to the audible command,and to identify and/or extract phrases from the acoustic transcript.Similarly, as shown in FIG. 7, the phrase extractor 710 may analyze theacoustic transcript 705 to identify and/or extract each phrasecomprising the spoken user command in accordance with a linguisticcontext of the acoustic model (e.g., the acoustic model 418) used togenerate the transcript. Accordingly, the phrase extractor 710 mayanalyze the acoustic transcript 705 to identify and/or extract one ormore phrases in Spanish.

In this example, the phrase extractor 710 may analyze a first portion ofacoustic transcript 705 corresponding to the spoken user command “Watch”to identify the appropriate and corresponding Spanish phrase. As shownin the example depicted in FIG. 4, the system (e.g., speech recognitionmodule) may be unable to identify a Spanish phrase corresponding to thespoken user command “Watch.” In some embodiments, the system may providean indication (e.g., visual indication, pop-up window, etc.) to the userthat the system was unable to identify and/or interpret a portion of thecommand data. Additionally or alternatively, the system may insert amarker or some other suitable indicator within the acoustic transcript(e.g., the acoustic transcript 705) indicating that the system wasunable to identify a Spanish word or phrase for a portion of the commanddata.

In some embodiments, the system may display on a display device one ormore phrases identified by the phrase extractor 710 that match thecommand data and/or acoustic transcript. Referring to the example above,although the system may be unable to identify a Spanish phrasecorresponding to the spoken user command “Watch,” the system may presentto the user a listing of Spanish phrases that match the spoken command.The system may prompt the user to indicate and/or confirm whether aportion of the audible command and/or acoustic transcript is identifiedwithin the listing of Spanish phrases displayed to the user. The systemmay retrieve a listing of phrases to be displayed to the user from alanguage database. Referring back to the example in FIG. 7, the phraseextractor 710 may then analyze a second portion of the acoustictranscript 705 corresponding to the spoken user command “Nickelodeon” toidentify the appropriate and corresponding Spanish phrase. As notedabove, in this example, the system has identified a Spanish phrase(i.e., “Meneca Lorient”) as the Spanish word/phrase that bestcorresponds to the spoken user command, i.e., “Nickelodeon.”

In some embodiments, the natural language processor may process andanalyze input from a rules database (e.g., the rules database 422, 711),which may store various rules (e.g., heuristic rules) governing theinterpretation of command data. In some embodiments, the system maydetermine which rules or groups of rules to utilize when interpretingcommand data based on the language spoken by the user and/or theacoustic models utilized to interpret the user's spoken commands. Forexample, if the system determines that a particular phrase/word wasspoken in a first language (e.g., English), the natural languageprocessor may retrieve (or utilize) rules from the rules database 422associated with governing English words/phrases. In some embodiments,the system may utilize heuristic rules when a user command was spoken inmultiple languages (e.g., Spanglish) and the system is distinguishingwhich identified words/phrases were spoken in a first language (e.g.,English language) or another language (e.g., Spanish language).

As shown on FIG. 4, another example of a heuristic rule that may bestored in the rules database 422, would be that in instances where thesystem determines that the name of a programming network (e.g., HBO,Showtime, NBC, etc.) is detected within command data, the system mayinfer that the user intends to request a channel change (i.e., “IfNetwork and Watch then action=Channel Name”). By identifying particularword(s)/phrase(s) within the spoken command of a user, the system maydetermine which set of heuristic rules should be utilized to determine(and/or identify) the remaining words/phrases comprising the commanddata.

Alternatively or additionally, when the system has identified, with ahigh level of confidence or certainty, one or more phrases/words of thecommand data in a first language, the identification of the one or morephrases/words may provide the system some guidance (via one or moreheuristic rules) as to how the phrases/words of the command data shouldbe interpreted for a second language. For instance, if the systemidentifies with a threshold confidence level (e.g., a 95% confidencelevel) that a “content entity” phrase in the command data is an Englishword, the system may infer that one or more of the remaining portions ofthe command data does not correspond to other “content entity” phrasesin the English language. Instead, the system may emphasize its analysisand interpretation of the remaining portions of the command data bysearching for a “content entity” phrase in Spanish and/or “actionentity” phrases in either Spanish, English, or another language.

As an example, the system may receive command data corresponding to thespoken user command “Ver+Brad Pitt,” and the system may analyze thecommand data using both an English-based acoustic model and aSpanish-based acoustic model to interpret the command data. Whenanalyzing the second portion of the command data, the system mayidentify the phrases/words “Brad Pitt” in both the English-basedacoustic transcript of the user's spoken command and a Spanish-basedacoustic transcript. The system may also identify that the phrase/words“Brad Pitt” corresponds to a content entity phrase given that thisparticular word/phrase represents the name of an actor. Utilizing one ormore heuristic rules, the system may subsequently infer with a highlevel of confidence that the detected actor's name corresponds to aword/phrase such that either the identified English phrase or theSpanish phrase may appropriately be utilized by the natural languageprocessor to determine a desired/intended operational command.Alternatively or additionally, when the system identifies identicalwords/phrases in multiple languages, the system may select theword/phrase corresponding to the default language utilized by the system(e.g., English).

Referring back to the example above, when the system receives commanddata corresponding to the spoken user command “Ver+Brad Pitt,” thesystem may analyze the first portion of the command data utilizing botha Spanish-based acoustic model and an English-based acoustic model togenerate an acoustic transcript of the command data in both Spanish andEnglish. When analyzing the Spanish-based acoustic model to generate anacoustic transcript of the command data in Spanish, the system mayidentify that the first portion of the command data best corresponds tothe Spanish word/phrase “Ver.” However, when analyzing the English-basedacoustic model to generate an acoustic transcript of the command data inEnglish, the system may identify that the first portion of the commanddata best corresponds to (or matches) the English word/phrase “Where.”Utilizing one or more heuristic rules, the system may determine that theword/phrase “Ver” may best correspond to an intended “action entity”phrase and not the term “Where” since the term “Ver” more appropriatelycorresponds to the intended user command based on the system's previousinterpretation of the content entity phrase “Brad Pitt” being either aSpanish phrase or an English phrase, and further, in view of theresulting phrase “Ver+Brad Pitt” fitting more appropriately within thecontext of an intended operational command for the computing device(e.g., the STB/DVR 113) than the alternative resulting phrase“Where+Brad Pitt.”

Referring back to FIG. 3A, at step 313, the system may begin a loop thatis performed for one or more of the phrases identified from the commanddata during step 311. In one embodiment, a natural language processingmodule, such as the NLP 420, may be configured to initiate a loop thatis performed for one or more of the phrases identified and/or extractedduring step 311. Additionally or alternatively, a computing deviceexecuting the natural language processing module may be configured tobegin a loop that is performed for one or more of the phrases identifiedand/or extracted from the command data during step 311. In step 315, foreach identified phrase analyzed within the loop, the system (and/or thenatural language processing module) may begin to determine a relevancyranking for the phrase being analyzed during step 313. As describedabove, the system may utilize a variety of parameters to rank phraseswithout departing from the scope of the present disclosure. For example,the system may rank (and/or re-rank) phrases based on synonyms used tomap or amend phrases/entities comprising an audible command. As anotherexample, the system may rank a phrase based on user input indicating aparticular ranking for the phrase. The system may also rank (and/ormodify a ranking) for a phrase based on the user's consumption historyand/or previous audible commands. As yet another example, the system mayrank (and/or modify a ranking) for a phrase based on a location of theuser. In this example, the system may utilize the content consumptionhistory for one or more users within a threshold proximity to thelocation of the user to rank and/or modify a ranking for a phrase. Thesystem may also rank and/or modify a ranking for a phrase based on theavailability of content items and/or whether the user has an entitlementto consume the content. For example, the system may provide a lowerranking for a phrase/entity corresponding to a content item that is on achannel or network that it not included in the user's contentsubscription. The system may utilize the above-discussed rankingtechniques either alone, or in combination, to provide rankings forphrases/entities comprising an audible command. The system mydynamically adjust the relevance or use of one or more rankingtechniques to maximize the ranking accuracy of the system and tominimize false positive results.

For example, referring back to FIG. 7, the phrase extractor 710 maytransmit data to a phrase ranker module (e.g., the phrase ranker 720)indicating the various phrases identified and/or extracted from eachacoustic transcript analyzed by the phrase extractor (e.g., the acoustictranscripts 701 and 705). The phrase ranker 720 may be configured torank each of the phrases received from the phrase extractor 710. Thephrase extractor 710 may transmit phrases identified in (and/orextracted from) each acoustic transcript for a variety of languages.Additionally or alternatively, the phrase ranker module may beconfigured to assign scores to each phrase indicating a respectiverelevancy ranking for the phrase.

In some embodiments, the phrase ranker 720 may utilize information fromvarious input sources (e.g., the heuristic rules 721, the click rankdatabase 722, etc.) to rank each of the phrases received from the phraseextractor 710. As discussed above, phrases can be ranked using a varietyof different parameters, such as phrase length, string similarity,entity clickranks, promotional content, consumption history, and thelike. The heuristic rules database 721 may store various rules governingthe interpretation and ranking of phrases identified by the phraseextractor 720. In some embodiments, the system may determine which rulesor groups of rules to use when interpreting extracted phrases based onthe underlying language of the acoustic transcript from which the phrasewas extracted. For example, if the system determines that a particularphrase/word was extracted from an English-based acoustic transcript, thenatural language process or may retrieve (and/or utilize) rules from theheuristic rules database 721 associated with ranking English words andphrases. For instance, a first heuristic rule may cause the phraseranker 720 to give higher priority to content with a larger “phraselength” than content with more “string similarity.” As another example,a second heuristic rule may cause the phrase ranker 720 to give higherpriority to “promotion content” than content with higher “click ranks.”As yet another example, a third heuristic rule may cause the phraseranker 720 to give higher priority to a manually added synonym thancontent with high “click ranks” and “sting similarity.”

In some embodiments, a click-rank database (e.g., the click-rankdatabase 722) may store click-rank data (and/or other suitableinformation indicating user viewing habits and selection history) thatmay be utilized by the system to identify previous user interactionswith the entertainment system and/or content service (e.g., previoususer content selections, times of day when the user utilizes the contentservice, etc.), as well content consumed by the user. The click-rankdatabase may store click rank data for the user providing the audiblecommand, as well as other users whose viewing and/or system interactionbehavior is being monitored by the content service. The phrase ranker720 may utilize the click rank data to analyze how other users haveinteracted with entertainment system (e.g., what operational commandswere performed by the system) based on similar audible commands thatwere processed by the users' respective systems. Additionally oralternatively, the phrase ranker 720 may utilize the click rank data toanalyze what content other users have requested based on similar audiblecommands that were processed by the users' respective systems.

The click rank database may store a log of a user's viewing behaviorthat is maintained by the user's respective entertainment system (e.g.,the STB/DVR 113). In some instances the STB/DVR 113 (or any othersuitable computing device operatively connected to the STB/DVR 113 forrecording user viewing behavior) may record which content a userconsumes and/or each viewing event associated with the user (e.g., eachuser click or selection via an electronic program guide or interface).By utilizing click rank data when identifying and ranking phrases, thephrase ranker 720 can more accurately determine the relevance of contententity phrases. For example, when ranking (and/or selecting between) twopossible hypotheses for television shows that best correspond to (ormatch) a phrase within a spoken user command, the phrase ranker 720 mayreference heuristic rules relating to the availability of theprogramming content (e.g., content items), such as which television showhas a start time that is closest in proximity to the current time.However, if both potential television shows (e.g., content hypotheses)have the same start time, the system may then reference click-rank datato determine the likely relevance (or rankings) of the two televisionshows.

The phrase ranker 720 may determine what content the user (or othergroups of users) have previously consumed, and use this information todetermine a relative relevancy or confidence for the two hypotheses forpotential television shows referenced in the user audible command byranking each hypothesis based on previous user viewing habits. Forexample, the phrase ranker 720 may assign a higher ranking (and/orconfidence level) to a first television show that was previously viewedby a higher number of users within a time period (e.g., the last month,the last year, on weekends, at night, etc.) as compared to a second (orother) television shows. As another example, the phrase ranker 720 mayassign a higher ranking (and/or confidence level) to a first televisionshow that was previously viewed by a larger population of users thathave issued similar audible commands as compared to the secondtelevision show. By ranking the different content entity phrasessupplied by the phrase extractor 710, phrase ranker may more accuratelydistinguish between potential content hypotheses based on determinedprobabilities and/or confidences. For example, content hypotheses thatare assigned (or designated) as having a high threshold level ofconfidence (and/or probability of being accurate) may cause theentertainment system to automatically select the highly-ranked contentitem. The system may be further configured to automatically tune to achannel to begin displaying the identified content item. Additionally oralternatively, content hypotheses that are assigned (or designated) ashaving a low threshold level of confidence may cause the system tofurther supplement (and/or calibrate) rankings and hypothesis selectionsmade available to the user. For example, the system may provide (via anapplication interface) to the user a listing of available choices (e.g.,content hypotheses) as to which particular content item the user wasreferring to in their spoken command. User selections may be storedand/or utilized to calibrate data within the click-rank database and/orfuture confidence levels for programming content. Referring now to FIG.3A, after determining relevancy rankings during step 315, the method mayreturn to step 313 to continue the loop until all of the analyzedphrases have been processed, and when those phrases have been processed,the method may proceed to step 307. During step 307, the system maycontinue the loop until all of the acoustic models determined duringstep 305 have been analyzed, and when those acoustic models have beenanalyzed, the method may proceed to step 317. For example, rather thaniteratively processing each identified acoustic model and eachacted/identified phrase as described above with reference to steps307-313, the system may be configured to simultaneously process commanddata across a plurality of acoustic models. In such embodiments, thesystem may identify/extract and rank a plurality of phrases comprisingthe acoustic transcripts that result from the command data beingprocessed by one or more acoustic models determined during step 305.

At step 317, the system may determine/assign one or more actionclassifiers (e.g., phrase classifications) for the one or more phrasesanalyzed during step 313. A natural language processing module (e.g.,the NLP 420) may determine/assign one or more action classifiers for theone or more phrases analyzed during step 313. The system may utilizeaction classifiers to categorize the various types of phrases/words thatmay be extracted from a spoken user command. For example, a first typeof action classifier may comprise a content entity, which may representone or more various types of content and/or content-related informationthat may be included in a user's spoken command. Various types ofcontent and/or content-related information may be categorized as acontent entity without departing from the scope of the presentdisclosure, such as the name of a content title, actor, sports team,network, and the like. As another example, a second type of actionclassifier utilized by the system may comprise an action entity, whichmay represent one or more types of actions and/or commands that a usermay intend for a computing device (e.g., the STB/DVR 113, the displaydevice 112) to implement, such as a device command. Various types ofactions and/or operational commands may be categorized as an actionentity without departing from the scope of the present disclosure, suchas commands to modify volume, change a channel, power-off or power-onthe entertainment system, watch a particular content item, retrieveinformation for a content item, and the like.

The system may require that command data corresponding to certain usercommands comprise both a content entity and an action entity. Forexample, if the user provides a spoken command to a computing device(e.g., the STB/DVR 113) to change the current channel to a differentchannel, the system may analyze the one or more phrases in the commanddata (representing the user's spoken command) to determine whichphrase(s) corresponds to an action entity (e.g., the command of changingthe channel) and which phrase(s) corresponds to a content entity (e.g.,the channel to which the device will subsequently tune). In thisexample, without the necessary content entity, the computing devicewould not be capable of performing the corresponding operationalcommand. Additionally or alternatively, after identifying an actionentity in the one or more phrases analyzed during step 313, the systemmay be configured to identify content entities in the one or morephrases extracted from the spoken user command.

Alternatively or additionally, after determining that a particular typeof phrase classification or action classifier corresponds to one or morephrases comprising the user's spoken command, the system may assign thedetermined phrase classification(s) or action classifier(s) to the oneor more phrases comprising the user's spoken command. The system may beconfigured to assign a phrase classification (or action classifier) toeach phrase and/or a combination of phrases comprising the user's spokencommand. The system may be further configured to generate a recognizedpattern (and/or template) based on the one or more phrases comprisingthe user's spoken command, the corresponding action classifier types forthe one or more phrases comprising the user's spoken command andcorresponding operational commands that a user desires/intends acomputing device to implement based on the spoken user command. Patternsgenerated by the system may be stored in a database (e.g., the patternsdatabase 732) for subsequent retrieval and or reference by the system.The pattern database may comprise the various types of actions andoperational commands that a user desires/intends the entertainmentsystem to implement based on various combinations of action entities andcontent entities. Additionally or alternatively, the patterns stored inthe pattern database may represent the operational commands that occurmost frequently based on the user's previous spoken commands. Thepattern database may rank the various patterns stored in the databasebased on a variety of factors (e.g., frequency) and heuristic rules,such that the system may more accurately determine intended operationalcommands for received command data based on the stored patterns.

For example, the system may generate a first pattern indicating that inthe event the command data comprises both the action entity phrase“Watch” and a content entity phrase corresponding to a movie title, thesystem may instruct the STB/DVR 113 (or any other suitable computingdevice) to begin showing the movie on the display device 112. In thisexample, the system may flag this particular pattern (e.g., acombination of the action entity “Watch” and a movie title) and storethe flagged pattern in the pattern database to be used by the system tobetter recognize/classify other user commands comprising similar actionclassifiers. As another example, the system may generate a secondpattern indicating that in the event the command data comprises both theaction entity phrase “Watch” and a content entity corresponding to thename of a sports team, the system may instruct the STB/DVR 113 to beginshowing a sporting event feature the desired sports team. As yet anotherexample, the system may generate a third pattern indicating that in theevent the command data comprises both the action entity phrase “Watch”and a content entity corresponding to the name of a TV program, thesystem may instruct the STB/DVR 113 to tune to a channel playing thedesired TV program.

In some embodiments, the system may calibrate or modify one or moregenerated patterns and/or patterns stored in the pattern database.Referring to the example above, if a user says “Watch Big Bang Theory,”the system may utilize the third generated pattern to tune to a channelplaying the show “Big Bang Theory.” However, in the event that the TVshow is not being currently shown on any television channels ornetworks, the system may calibrate the pattern to instruct theentertainment system to implement an alternative operational command.For example, the system may instruct the entertainment system to displayan entity page for the TV program (i.e., the “Big Bang Theory” entitypage), which may provide the user with various options relating to theTV program, such as options to record future episodes, access previousepisodes of the program via video-on-demand services, and the like. Inthis example, the system may calibrate the generated pattern to firstattempt to tune to a television channel showing the TV program, and ifunavailable, to display an entity page for the TV program.

Additionally or alternatively, the system may calibrate the generatedpattern based on the viewing habits of other users. Referring back tothe example above, the system may modify the generated pattern toimplement certain operational commands based on the type of operationalcommands that have been implemented when other users have providedsimilar commands to their respective. For example, the system mayutilize access information stored in the click-rank database todetermine what operational commands are implemented by userentertainment systems when the user requests to “Watch Big Bang Theory.”The system may use this information to calibrate the pattern generatedfor the present user. In this example, the system may determine thattypically when a user requests to watch “Big Bang Theory” in themorning, the TV show is not being shown on any television networks, andthus the user is presented with the “Big Bang Theory” entity page ratherthan tuning to the TV program. Accordingly, if the present user issues acommand to “Watch Big Bang Theory” in the morning, the system may firstattempt to show the user the “Big Bang Theory” entity page beforeattempting to tune to the TV program. The system may provide the userwith an application interface that permits the user to manuallycalibrate one or more recognized patterns. The system may receive userinput selections identifying an order of potential operational commandsthat may be associated with a particular pattern. The system may storethe user input selections and utilize the user feedback to modify and/orcalibrate patterns stored in the pattern database.

As shown in FIG. 7, the phrase ranker 720 may transmit data to actionclassifier module (e.g. the action classifier 730) indicating therespective rankings for various phrases identified and/or extracted fromeach acoustic transcript analyzed by the phrase extractor 710. Theaction classifier 730 may be configured to generate expectedclassifications for the various phrases that have been ranked by thephrase ranker 720. Additionally or alternatively, the action classifier730 may be configured to generate an expected classification for aphrase based on whether the phrase corresponds to and/or is associatedwith a content entity (e.g., content title, television channel, sportsteam, etc.). The action classifier 730 may be further configured torecognize the particular type of content entity corresponding to and/orassociated with a phrase, such as whether the phrase corresponds to acontent title, whether the phrase corresponds to a television channeland the like. The action classifier 730 may be configured to specify, ina classification for a phrase, the particular type of content entitycorresponding to and/or associated with the phrase. For example, theaction classifier 730 may determine that a particular phrase correspondsto a content entity indicating a content title. The action classifier730 may be further configured to generate an expected classification fora phrase based on whether the phrase corresponds to and/or is associatedwith an action entity and/or operational command (e.g., change channel,record, activate menu etc.). Like content entities, the actionclassifier 730 may be configured to recognize the particular type ofaction entity corresponding to and/or associated with a phrase.

The action classifier 730 may utilize information from various inputsources (e.g., the heuristic rules 731, the pattern database 732, etc.)to classify each of the phrases. In some embodiments, the actionclassifier module may be configured to associate in a database (or othersuitable form of data storage) each phrase with its correspondingclassification. Referring back to the examples in FIGS. 4 and 7, thespoken user command corresponding to “Watch Nickelodeon” may becategorized by the system (e.g., the action classifier 730) ascomprising both an action entity phrase and a content entity phrase. Inthis example, the phrase “Nickelodeon” corresponds to a content entityrepresenting a particular programming network, and the phrase “Watch”corresponds to an action entity representing an operational command fora computing device (e.g., the STB/DVR 113). The action classifier 730may retrieve a pattern (e.g., command pattern) from the pattern database732 governing operational commands for the identified phrases andcorresponding action classifiers (e.g., phrase classification). In thisexample, the system may identify a pattern corresponding to anoperational command for a computing device (e.g., the STB/DVR 113) totune to a channel corresponding to the television network Nickelodeon.The system may utilize the retrieved command patterns to map identifiedphrases for the purpose of resolving the action/intent underlying theuser's spoken command. The system may generate and store a variety ofcommand patterns in the pattern database 732 based on a history ofcommands spoken by a user, without departing from the scope of thepresent disclosure. For example, a first command pattern stored in thepattern database may comprise:

${IF}\mspace{14mu} \begin{pmatrix}{RECORD} \\{\langle{series}\rangle} \\{\langle{channel}\rangle}\end{pmatrix}\mspace{14mu} {THEN}\mspace{14mu} \begin{pmatrix}{{action} = {RECORD}} \\{{channel} = {\langle{channel}\rangle}} \\{{title} = {\langle{series}\rangle}}\end{pmatrix}$

In this example, the system may compare command data to a commandpattern retrieved from the pattern database 732. If the systemdetermines that the audible command comprises the phrase “RECORD”followed by two content entity phrases (i.e., “series” and “channel”),the system may be configured to correlate the spoken user command datato an operational command for the computing device (e.g., the STB/DVR113) to record a content item corresponding to the identified “series”broadcasted on the identified “channel.” In some instances, the systemmay compare a first phrase to a database of words or phrases to identifywhether the first phrase corresponds to a particular type of contententity phrase, such as a movie title, a sports team, a channel, a seriestitle, a genre, an asset type, and the like. As another example, asecond command pattern stored in the pattern database may comprise:

${IF}\mspace{14mu} \begin{pmatrix}{GET} \\{\langle{genre}\rangle} \\{\langle{assetType}\rangle}\end{pmatrix}\mspace{14mu} {THEN}\mspace{14mu} \begin{pmatrix}{{Action} = {BROWSE}} \\{{genre} = {\langle{genre}\rangle}} \\{{assetType} = {\langle{assetType}\rangle}}\end{pmatrix}$

In the example above, if the system determines that the audible commandcomprises the phrase “GET” followed by two content entity phrases (i.e.,“genre” and “asset type”), the system may be configured to correlate thespoken user command data to an operational command for the computingdevice (e.g., the STB/DVR 113) to browse a plurality of content itemscorresponding to the identified “genre” (e.g., horror, comedy, etc.) and“asset type” (e.g., movie, song, TV show, etc.). In this example, if thesystem determines that the command data corresponds to the phrase“Get+Horror+Movie,” the system may identify and display in a userinterface a plurality of content items corresponding to horror moviesfor the user the user to navigate. Similarly, if the system determinesthat the command data corresponds to the phrase “Get+Horror+Cowboys,”the system may recognize that the term “Cowboys” does not correspond toan asset type, and as such, the phrase corresponding to the user'sspoken command does not match the predefined syntax of the selectedpattern. Accordingly, in some embodiments, the system may then attemptto identify retrieve a new pattern stored in the pattern database 732 toresolve the user's spoken command. Additionally or alternatively, thesystem may generate a new pattern based on the user's spoken command.The system may query the user to determine the particular operationalcommand the user is attempting to perform. The system may associate theintended/targeted operational command with a new pattern, and store thepattern in the pattern database 732.

Referring to the example embodiment in FIG. 5, the English-basedacoustic transcript associated with the command data (e.g., “Where+UnitVision”) may be analyzed by the system to determine the appropriateaction classifiers. In this example, the system may be unable toidentify action classifiers corresponding to the word/phrase “UnitVision,” but may identify the phrase “Where” as an action classifier.With respect to the Spanish-based acoustic transcript associated withthe command data (e.g., “Ver Univision”), the system may analyze thesewords/phrases to determine the appropriate action classifiers. In thisexample, Spanish-based acoustic transcript may be categorized by thesystem as comprising both an action entity phrase and a content entityphrase. In this example, the phrase “Univision” corresponds to a contententity representing a particular programming network, and the phrase“Ver” corresponds to an action entity representing an operationalcommand for a computing device (e.g., the STB/DVR 113). In this example,using the assigned phrase classifications for the phrases comprising therespective English-based and Spanish-based acoustic transcripts, thesystem may identify a pattern corresponding to an operational commandfor a computing device (e.g., the STB/DVR 113) to tune to a channelcorresponding to the television network Univision.

Referring to the example embodiment in FIG. 6, the English-basedacoustic transcript associated with the command data (e.g., “Watch+AchBay Oh”) may be analyzed by the system to determine the appropriateaction classifiers (e.g., phrase classifications). In this example, theEnglish-based acoustic transcript may be categorized by the system ascomprising at least an action entity phrase. In this example, the phrase“Ach Bay Oh” may be unrecognizable to the system, while the phrase“Watch” corresponds to an action entity representing an operationalcommand for a computing device (e.g., the STB/DVR 113). The system mayprompt the user to repeat the spoke command so that the system mayattempt to recognize the unrecognizable phrase. The system may providethe user with a listing of the content entities and/or action entitiesthat most closely match the unrecognizable phrase. With respect to theSpanish-based acoustic transcript associated with the command data(e.g., “<unrecognizable>+HBO”), the system may analyze thesewords/phrases to determine the appropriate action classifiers. In thisexample, Spanish-based acoustic transcript may be categorized by thesystem as comprising at least one content entity phrase. In thisexample, the phrase “HBO” corresponds to a content entity representing aparticular programming network; however a first portion of the acoustictranscript is unrecognizable to the system. The system may prompt theuser to repeat the spoke command so that the system may attempt todecipher the unrecognizable phrase. The system may provide the user witha listing of the content entities and/or action entities that mostclosely match the unrecognizable phrase.

Referring now FIG. 3B, after determining action classifiers during step327, the method may proceed to step 321, where the system may determineone or more matches for the one or more phrases analyzed during step313. Additionally or alternatively, the system may process the phraserankings and/or action classifiers assigned to the one or more phrasesanalyzed during step 313 to determine a subset and/or combination ofwords/phrases that best represent the intended operational commanddesired by the user. The system may identify a subset or combination ofwords/phrases from one or more different acoustic models (and/oracoustic transcripts) when determining one or more match phrases. Forinstance, referring to the example in FIG. 6, the system may utilize thephrase “Watch” as determined by the English-based acoustic model 615 andthe phrase “HBO” as determined by the Spanish-based acoustic model 618when generating a match phrase corresponding to the spoken user command.This particular match phrase may correspond to an operational command(in Spanglish) to tune to the television network HBO.

Additionally or alternatively, multiple match phrases may be determinedand subsequently considered by the system. Referring back to the examplein FIG. 6, in the instance that the system is able to identify a contentitem corresponding to the phrase “Ach Bay Oh,” such as the name of anactor, the system may determine a second match phrase by utilizing thephrases “Watch” and “Ach Bay Oh” as determined by English-based acousticmodel 615. This particular match phrase may correspond to an operationalcommand for the system to display the entity page for the actor Ach BayOh, which may provide the user with a listing of content items availablefor consumption that feature the actor Ach Bay Oh.

At step 323, the system may determine a response score for one or moreof the match phrases determined during step 321. A response filtermodule (e.g., response filter 740) may be utilized to determine responsescores for the match phrases determined during step 321. The responsefilter module may generate a response score for each command hypothesis(e.g., match phrase) that was determined by the system. The responsefilter module may be configured to utilize and/or combine other scoresand information associated with the various match phrases during theprevious actions/steps of the natural language processing algorithmdepicted in FIG. 7. The response filter module may utilize the otherscores (e.g., phrase rankings) and other information ascertained duringthe previous actions/steps of the natural language processing algorithm(e.g., action classifier information, generated/corresponding patterns,etc.) to determine a score (e.g., response score) for the match phrase.

After determining and/or retrieving a response score for the matchphrase determined during step 323, the method may proceed to step 325,where the system may determine whether the response score for the matchphrase analyzed during step 323 satisfies a threshold value. The systemmay determine whether a response score for each of the match phrasedetermined during step 321 satisfies a threshold value. The system mayutilize a default threshold value set by the content provider.Additionally or alternatively, the user may determine (and/or modify)the threshold value via an application interface provided by the contentor service provider. If the response score for a match phrase determinedduring step 323 does not satisfy the threshold value, the method proceedto step 328, where the system may discard the match phrase beinganalyzed during step 325. After the system discards the match phrase,the method may proceed to step 329. If the response score for a matchphrase being analyzed during step 325 satisfies the threshold value, themethod proceed to step 327, where the system may add the match phrase toa response array. The response array may comprise a list of NLPresponses which have a confidence score satisfying a threshold value.The system may execute one or more responses in the array if subsequentmodules within the system flag the response as usable/appropriate. Theresponses included in the response array may represent just a possibleintent of the user and in some instances, may not guarantee theexecution of the user's intended operational command. As will beexplained in more detail below, in some embodiments, other modules inthe system may identify and/or populate the valid intents with moreinformation such that a module in the system (e.g., the action responsemodule 440) may correctly execute the user's audible command.

Referring back to the example embodiment in FIG. 6, the system maydetermine response scores for the two match phrases (e.g., commandhypotheses) identified by the system. In this example, the system maydetermine/assign a first response score to the match phrase comprisingthe word “Watch” (as determined by the English-based acoustic model 615)and the word “HBO” (as determined by the Spanish-based acoustic model618). The system may further determine/assign a second response score tothe match phrase comprising the phrase “Watch+Ach Bay Oh” as determinedby the English-based acoustic model 615. The system may then compareeach of the first and second response scores to a threshold value todetermine whether either match phrase may be discarded.

Referring now to FIG. 7, the action classifier 730 may be configured totransmit data to response filter module (e.g. the response filter 740)indicating the respective classification and/or correspondingcharacteristics assigned to various phrases identified and/or extractedfrom each acoustic transcript analyzed by the phrase extractor 710. Theaction classifier 730 may also transmit other scores (e.g., phraserankings) and information ascertained during the previous actions/stepsof the natural language processing (e.g., action classifier information,generated/corresponding patterns, etc.) to the response filter 740. Asdiscussed above, the response filter 740 may utilize the informationreceived from action classifier 730, as well heuristic rules retrievedfrom the heuristic rules database 741, to generate a response score foreach match phrase. The response filter 740 may utilize rules retrievedfrom the database 741 to analyze and filter certain phrases from a“response array” if certain conditions are found to be true.Additionally or alternatively, the response filter 740 may filter outthose phrases that do not satisfy a threshold confidence level.

The response filter 740 may process response scores for each matchphrase to determine whether the response score for each match phrasesatisfies a threshold value, and may further be configured to discardthose match phrases having response scores that fail to satisfy thethreshold value. For those match phrases that have response scores thatsatisfy the threshold value, the response filter 740 may store suchmatch phrases to a database or other form of storage. The heuristicrules database 741 may include a plurality of rules and/or parametersgoverning the processing of response scores for the purpose ofdetermining invalid hypotheses. For example, a first heuristic rulestored in the rules database 741 may comprise keeping receivedhypotheses above a first threshold level of confidence. Anotherheuristic rule stored in the rules database 741 may comprise keepingreceived hypotheses associated with promotional content. Yet anotherheuristic rule stored in the rules database 741 may comprise filteringout (or discarding) hypotheses associated with content items havingadult-themed material. Still another heuristic rule stored in the rulesdatabase 741 may comprise filtering out (or discarding) hypotheses thatdo not satisfy the syntax of a pattern stored in the pattern database732. A variety of heuristic rules may be stored in the rules database741 without departing from the scope of the present disclosure.

In some embodiments, after filtering and/or processing the matchphrases, the natural language processor (e.g., the NLP 420) may transmitthe data indicating the results of the filtering process to anothercomputing device and/or module. For example, as shown in FIG. 4, the NLP420 may transmit output from the response filter 740 to the selector430. In this example, the selector 430 may be configured to processcontent entities within one or more match phrases to determine whetherthe user is authorized to consume the corresponding programming content.The selector 430 may compare data indicating parental security settingsof the entertainment system to the content entity contained in a matchphrase. In this example, if the match phrase includes a content entityphrase corresponding to a movie having adult themes and/or an explicitcontent rating, the system may prevent the user from accessing therequested content. As another example, the selector 430 may compare dataindicating a user's content subscription to the content entity containedin a match phrase. In this example, if the match phrase includes acontent entity phrase corresponding to a television network that is notincluded in the user's content subscription, the system may prevent theuser carrying out the desired operational command.

At step 329, the system may determine whether any additional matchphrases identified and/or determined during step 321 are to be analyzed.As noted above, in some embodiments, the system may analyze each of thematch phrases determined during step 321. If the system determines thatan additional match phrase determined during step 321 may be analyzed,the method may proceed to step 323, where the system may determine aresponse scores for another match phrase determined during step 321. Ifthe system determines that no additional match phrases may be analyzed,the method may proceed to step 331, where the system may determine anaction response for a match phrase that was added to the response arrayduring step 327. The system may determine an action response for a matchphrase in the response array having the highest response score.

For example, referring to FIG. 4, action response module 440 may processoutput from selector 430 to determine an action response for one or morephrases processed by the NLP 420. In some embodiments, action responsemodule 440 may process output from the NLP 420 to determine a firstmatch phrase having a highest response score, and may then determine theappropriate action response for the first match phrase. The actionresponse module 440 may transmit an instruction (or request) to acomputing device, (e.g., the STB/DVR 411, the gateway device 111) toperform an operational command corresponding to at least one of the oneor more phrases processed by the NLP 420, such as the match phrasehaving the highest response score. Additionally or alternatively, theaction response module (or another suitable computing device) may beconfigured to utilize an application program interface (API) of acomputing device (e.g., the STB/DVR 113, the gateway device 111, theapplication server 107) to perform the operational command correspondingto a match phrase determined by the system. An API request generated bythe action response module may be configured to cause the API toinstruct a computing device (e.g., the STB/DVR 411) to initiate/performa variety of operational commands, such as retrieving programmingcontent, displaying a particular menu/interface on a display device,changing a channel on a tuner (or other suitable computing device), andthe like. The action response module may generate various types ofrequest for the user based on the operational command corresponding tothe match phrase without departing from the scope of the presentdisclosure, such as an Experience API (xAPI) request or other suitableAPI requests. The action response module may be configured to transmitthe generated request/call to an appropriate computing device (e.g., theSTB/DVR 113, the STB/DVR 411) to execute/perform the correspondingoperational command.

The action response module may be configured to store the actionresponse determined during step 331 in memory or some other suitableform of storage. Additionally or alternatively, the action responsemodule may be configured to associate the determined action responsewith the corresponding match phrase in a database. The system mayutilize a history of determined action responses and corresponding matchphrases for user spoken commands to calibrate the speech recognitionsystem and increase the accuracy of future action responsedeterminations made for spoken user commands.

At step 335, the system may execute one or more operational commandscorresponding to an action response and/or API request generated duringstep 331. As discussed above, the system may instruct a computing device(e.g., the STB/DVR 113) to execute one or more operational commandscorresponding to the action response and/or API request determinedduring step 331. For example as depicted by element 408, in FIG. 4, theaction response module 440 may transmit an API request/call to the xAPI450 to cause an appropriate computing device (e.g., the STB/DVR 411) toexecute a desired operational command. Although the example in FIG. 4depicts an Experience API (e.g., the xAPI 450, 550, 650), any suitableapplication program interface may be utilized by the system to execute adesired operational command without departing from the scope of thepresent disclosure. As depicted by element 409 in FIG. 4, using therequest/call transmitted from the action response module 440, the xAPI450 may be configured to transmit a request/call to an appropriatecomputing device (e.g., (e.g., the STB/DVR 411) to perform the desiredoperational command, such as tuning to the STB/DVR 411 to a channelcorresponding to the television network Nickelodeon.

In some embodiments, during step 335 the API (e.g., xAPI) may beconfigured to generate a runtime environment call to execute anoperational command corresponding to the action response and/or APIrequest determined during step 331. Referring to the example in FIG. 4,xAPI may generate a runtime environment call, such as a cross-platformruntime environment call (XRE), to the STB/DVR 411 to tune to a channelcorresponding to the television network Nickelodeon. Referring now toFIG. 5, as another example, the action response module 540 may transmitan API request/call to the xAPI 550, which may then generate an XRE callto instruct the STB/DVR 511 to tune to a channel corresponding to thetelevision network Univision. Referring now to FIG. 6, as yet anotherexample, action response module 640 may transmit a request/call to thexAPI 650, which may then generate an XRE call to instruct the STB/DVR611 to tune to a channel corresponding to the television network HBO.The request (e.g., API call) transmitted to the API (e.g., the xAPI 450)may require user authentication prior to the API generating aninstruction, such as a runtime environment call, to the appropriatecomputing device for performing the desired operational command.

The foregoing description of embodiments has been presented for purposesof illustration and description. The foregoing description is notintended to be exhaustive or to limit embodiments to the precise formdisclosed, and modifications and variations are possible in light of theabove teachings or may be acquired from practice of various embodiments.The embodiments discussed herein were chosen and described in order toexplain the principles and the nature of various embodiments and theirpractical application to enable one skilled in the art to utilize thepresent disclosure in various embodiments and with various modificationsas are suited to the particular use contemplated. All embodiments neednot necessarily achieve all objects or advantages identified above. Anyand all permutations of various features described herein are within thescope of the present disclosure.

We claim:
 1. A method comprising: receiving, from an input device,command data corresponding to a spoken user command; determining, by aprocessor, a plurality of acoustic models to process the command data,wherein at least a first acoustic model of the plurality of acousticmodels corresponds to a first language, and a second acoustic model ofthe plurality of acoustic models corresponds to a second language;generating, by the processor and based on the command data, a firstacoustic transcript of the spoken user command using the first acousticmodel, and a second acoustic transcript of the spoken user command usingthe second acoustic model; generating, from the first acoustictranscript, a first plurality of phrases; generating, from the secondacoustic transcript, a second plurality of phrases; assigning a phraseclassification for each phrase of the first plurality of phrases and thesecond plurality of phrases; determining, by the processor and based atleast in part on the assigned phrase classifications, one or more matchphrases, wherein each match phrase comprises two or more phrases fromone or more of the first plurality of phrases and the second pluralityof phrases; determining a response score for each match phrase of theone or more match phrases; determining a first match phrase of the oneor more match phrases comprising a highest response score; andtransmitting, to a computing device, an operational commandcorresponding to the determined first match phrase.
 2. The method ofclaim 1, wherein determining the plurality acoustic models to processthe command data further comprises: receiving a user input selectionindicating the first acoustic model.
 3. The method of claim 1, whereindetermining the plurality of acoustic models to process the command datafurther comprises: receiving a user input selection indicating the firstlanguage.
 4. The method of claim 1, further comprising: comparing eachphrase of the first plurality of phrases and the second plurality ofphrases to a plurality of command patterns; and determining, by theprocessor, at least a first operational command based on the comparisonof each phrase of the first plurality of phrases and the secondplurality of phrases to a first command pattern of the plurality ofcommand patterns.
 5. The method of claim 1, wherein determining theplurality of acoustic models to process the command data furthercomprises: determining, by the processor, an acoustic model based atleast in part on one of a content consumption history of a user and alocation of the user.
 6. The method of claim 1, wherein the firstacoustic model comprises a Spanish acoustic model, and the secondacoustic model comprises an English acoustic model.
 7. The method ofclaim 1, wherein assigning the phrase classification for each phrase ofthe first plurality of phrases and the second plurality of phrasesfurther comprises: determining that a first phrase of the firstplurality of phrases corresponds to a first type of phraseclassification; and assigning one or more remaining phrases of the firstplurality of phrases to correspond to a second type of phraseclassification.
 8. The method of claim 7, wherein the first type ofphrase classification comprises an action entity indicating a devicecommand.
 9. The method of claim 1, wherein generating the first acoustictranscript of the spoken user command using the first acoustic modelfurther comprises: comparing a first portion of the command data to adatabase of voice templates; and determining, by the processor and basedon the first acoustic model, a first voice template corresponding to thefirst portion of the command data.
 10. A method comprising: receiving,from an input device, command data corresponding to a spoken usercommand; determining, by a processor, a plurality of acoustic models toprocess the command data; for each acoustic model of the plurality ofacoustic models: generating, by the processor, an acoustic transcriptcorresponding to the spoken user command; and generating, from theacoustic transcript, a plurality of phrases; determining, by theprocessor, one or more match phrases, wherein each match phrasecomprises two or more phrases from the plurality of generated phrases;determining a response score for each match phrase of the one or morematch phrases; determining that the response score for a first matchphrase of the one or more match phrases satisfies a threshold value; andtransmitting, to a computing device, an operational commandcorresponding to the first match phrase.
 11. The method of claim 10,wherein determining the one or more match phrases further comprises:assigning a phrase classification for each phrase of the plurality ofgenerated phrases; and determining the one or more match phrases basedat least in part on the assigned phrase classifications.
 12. The methodof claim 10, wherein determining the plurality of acoustic models toprocess the command data further comprises: determining, by theprocessor, an acoustic model based at least in part on one of a contentconsumption history of a user and a location of the user.
 13. The methodof claim 10, further comprising: comparing each phrase of the pluralityof generated phrases to a first command pattern; and determining, by theprocessor, at least a first operational command based on the comparisonof each phrase of the plurality of generated phrases to the firstcommand pattern.
 14. The method of claim 13, wherein the first commandpattern comprises a first content entity indicating a content item and afirst action entity indicating a first device command, the methodfurther comprising: determining that the content item is unavailable;and modifying the first command pattern to comprise a second actionentity indicating a second device command.
 15. The method of claim 10,wherein generating the acoustic transcript further comprises: comparinga first portion of the command data to a database of voice templates;and determining, by the processor and based on the acoustic transcript,a first voice template corresponding to the first portion of the commanddata.
 16. The method of claim 15, further comprising: receiving userinput selection indicating a first acoustic model in the plurality ofacoustic models.
 17. A method comprising: receiving, from a firstcomputing device, a plurality of acoustic transcripts corresponding to aspoken user command, each transcript comprising a transcription of thespoken user command based on an acoustic model; for each acoustictranscript of the plurality of acoustic transcripts: generating, fromthe acoustic transcript, a plurality of phrases; determining, by aprocessor, a ranking for each phrase of the plurality of generatedphrases; and assigning, based in part on the determined ranking, aphrase classification for each phrase of the plurality of generatedphrases; determining, by the processor and based in part on the assignedphrase classifications, one or more match phrases, wherein each matchphrase comprises two or more phrases from the plurality of generatedphrases; and transmitting, to a second computing device, an operationalcommand corresponding to a first match phrase of the one or more matchphrases.
 18. The method of claim 17, further comprising: comparing eachphrase of the plurality of generated phrases to a first command pattern;and determining, by the processor, at least a first operational commandbased on the comparison of each phrase of the plurality of generatedphrases to the first command pattern.
 19. The method of claim 17,wherein determining the ranking for each phrase of the plurality ofgenerated phrases further comprises: determining the ranking based atleast in part on a content consumption history of a first user.
 20. Themethod of claim 17, wherein determining the ranking for each phrase ofthe plurality of generated phrases further comprises: determining theranking based at least in part on an availability of programmingcontent.